Algorithm analysis method for auxiliary materials of photovoltaic panels

What is the role of artificial intelligence in photovoltaic technology?

Novel algorithms and techniques are being developed for design, forecasting and maintenance in photovoltaic due to high computational costs and volume of data. Machine Learning, artificial intelligence techniques and algorithms provide automated, intelligent and history-based solutions for complex scenarios.

How reliable are photovoltaic algorithms?

In summary, the algorithms' performance reveals a diverse landscape of accuracy, convergence, and efficiency. While some algorithms shine in specific models, the OBEDO, MGTO, and HDE algorithms consistently exhibit robust and dependable performance across various photovoltaic models.

How to conduct a photovoltaic system?

There are several methods have been used to conduct a photovoltaic system, e.g., Maximum Power Point Tracking, Artificial Neural Network model, Extreme Learning Machine, and Support Vector Machine, among others models.

How to model PV arrays based on AI techniques?

Almomani et al. . presented a method for modelling PV arrays based on AI techniques, specifically Genetic Algorithm (GA) and Cuckoo Optimisation Algorithm (COA). The adopted models using GA and COA were implemented in a simulation platform using MATLAB environment for two-diode and two-diode and single-diode models.

What are the applications of AI in solar photovoltaic systems?

In particular, methods using the AI approach for the following applications are discussed: prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems. Content may be subject to copyright. Content may be subject to copyright. Copyright © 2013, IGI Global.

Which algorithms are used to estimate PV parameters?

Metaheuristics are the most used techniques to estimate PV parameters. Different kinds of algorithms have been evaluated in recent years. The work in [ 112] compares different evolutionary algorithms, comparing Genetic Algorithms [ 48 ], Particle Swarm Optimization [ 52] and differential evolution [ 113 ].